Modeling HIV-1 Intracellular Replication - Universiteit van Amsterdam

78 Pages·2009·1.12 MB·English

by  Narges Zarrabi

Modeling and Simulation of HIV-1
Intracellular Replication
MSc Thesis
Author
Narges Zarrabi
Supervisor:
Prof. Dr. Peter M.A. Sloot
Submitted to Faculty of Science in partial fullfilment of the requirments for
the degree of Master of Science in Grid Computing, Computational Science
Computational Science
Faculty of Science
University of Amsterdam
UNIVERSITEIT VAN AMSTERDAM
2009
Modeling and Simulation of HIV-1
Intracellular Replication
MSc Thesis
Author
Narges Zarrabi
Supervisor:
Prof. Dr. Peter M.A. Sloot
Committee Members:
Prof. Peter Sloot
Prof. Jaap Kaandorp
Dr. Inge Bethke
Mr. Emiliano Mancini (MSc)

Abstract
Modeling HIV infection has a long history since 1980s when HIV was discovered and the
AIDS symptomes were first observed.
In recent decades, treating Human Immunodeficiency Virus infection and AIDS has
been the main research topic of many experiments in laboratories around the world. In
spite of the broad effort in designing antiretroviral drugs for treating HIV patients, there
is still no specific vaccine or drug available to completely block the virus replication. Many
mathematicalandcomputationalmodelshavebeendevelopedtoinvestigatethecomplexity
of HIV dynamics, immune response and drug therapy.
The objective of this thesis is to model the intracellular replication cycle of HIV-1. The
model is basically designed to study HIV replication inside a single cell before initiation of
drug therapy. Two modeling approaches have been used to implement the virus replication
model:Rate-limited approach and diffusion-limited approach. Results of the model are
discussed based on the number of cDNA created and integrated into the host cell DNA,
the number of viral mRNA transcribed, and the amount of translated viral proteins after
a single round of virus replication. Model validation is demonstrated graphically and
quantitatively based on comparison between simulation results and available experimental
data. A sensitivity analysis is also performed on some of the parameters used in the
model. This analysis helps us to determine which steps, in the viral replication process,
are more sensitive and uncertain. These results also indicate which parameters are critical
in the replication process and which parameters are less sensitive. Results obtained from
the simulation give insights about the detail of HIV replication dynamics inside the cell
at protein level. Therefore the model can be used for future studies of HIV intracellular
replication in vivo and drug treatment.
ii
Acknowledgments
First and foremost, I would like to take this opportunity to express my most sincere grat-
itude and appreciation to my supervisor, Professor Peter Sloot, for his countless efforts in
guiding and encouraging me throughout my research. He has supported me and given me
one of the best opportunities in my academic life to accomplish this research thesis. His
knowledgeable suggestions and heuristic advice always inspired me on my way.
This work was made possible through the ViroLab project (www.virolab.org) and I am
grateful to all ViroLab group members, especially Dr. Victor Muller and Dr. David van
de Vijver. I am also grateful to Dr. Tay Joc Cing and Assoc. Prof. Ong Yew Soon from
Nanyang Technological University.
I would like to thank Emiliano for his great helps throughout this thesis and the model
and also thank to Gokhan and Rick for their feedbacks on the model.
Again thanks to all my friends in Amsterdam including: Gokhan, Emiliano, Rick, Chinh,
David, Toktam, Erik and all the people in the Section of Computational Science who made
it a wonderful experience for me at SCS and Universiteit van Amsterdam.
Last and not least, I would like to thank my parents for their endless love, a special
thanks to beloved Shayan for his countless support, and thank to all my family and friends
back in Iran who encouraged and helped me during my whole life.
Contents
Abstract i
Acknowledgments iii
1 Introduction 1
1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Scope and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Literature Review 5
2.1 HIV Structure and Classification . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 HIV-1 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
HIV-1 Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
HIV-1 Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 HIV-1 Subtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 HIV-1 Replication and Intracellular Growth . . . . . . . . . . . . . . . . . 9
2.2.1 HIV Replication cycle . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 Replication Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.3 Replication Dynamics in Lymph Node . . . . . . . . . . . . . . . . 14
2.3 Existing Models of HIV Replication . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Model Design and Implementation 19
3.1 General Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 States and Transitions . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.2 Cell Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Rate-limited Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.1 Model Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.2 Parameters and Assumptions . . . . . . . . . . . . . . . . . . . . . 25
3.3 Diffusion-limited Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.1 Model Entities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.2 Parameters and Assumptions . . . . . . . . . . . . . . . . . . . . . 30
3.4 Model Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4 Simulation Results, Analysis, and Validation 35
4.1 Primary Results and Validation . . . . . . . . . . . . . . . . . . . . . . . . 36
4.1.1 Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Nuclear Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Nuclear Transfer Rate . . . . . . . . . . . . . . . . . . . . . . . . . 43
Integration Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Transcription Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Comparison of the Two Approaches . . . . . . . . . . . . . . . . . . . . . . 50
v
4.3.1 Simulation Performance . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5 Conclusions and Future Work 55
5.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2 Recommendations for Future Work . . . . . . . . . . . . . . . . . . . . . . 56
Bibliography 59
vi
List of Figures
2.1 HIV-1 structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 HIV genome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Different subtypes of HIV-1 . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 HIV intracellular replication cycle . . . . . . . . . . . . . . . . . . . . . . . 10
2.5 Three steps in HIV reverse transcription . . . . . . . . . . . . . . . . . . . 11
2.6 HIV infected CD4+ T cells in lymph node . . . . . . . . . . . . . . . . . . 15
3.1 7 main steps of HIV replicayion process . . . . . . . . . . . . . . . . . . . . 20
3.2 Cell states and Transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Proportion of cells getting infected under different MOIs . . . . . . . . . . 23
3.4 Cell internal variables and intracellular rates . . . . . . . . . . . . . . . . . 25
3.5 Flowchart of the rate-limited model . . . . . . . . . . . . . . . . . . . . . . 28
3.6 Simulation visualization of early stages of simulation: viral RNAs (red)
are assigned to the cell cytoplasm environment. The purple circles are the
cellular tRNA available in the cell . . . . . . . . . . . . . . . . . . . . . . . 31
3.7 Simulation visualization of late stages of simulation: viral mRNA (small
green) mainly in the cell nucleus, and translated viral proteins (big green)
in the cell cytoplasm. The purple circles are the cellular tRNA available in
the cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31